Dimensionality Reduction with PCA: Using Machine Learning

In this study, the Principal Component Analysis (PCA) method is examined to decrease dimensions in classification works. The intention here is to reduce the number of features so that our machine learning model will perform much better.

Methodology:

1. Data Collection: In our example dataset having multiple columns for a classification type of
2. Data Pre-processing: data need to be transformed in the right format as well as they must follow same scale before
3. Dimensionality Reduction: Dimension means feature/variable, so reducing the no of features is called asandardizing dimensionality reduction using PCA or in simple terms to reduce dimensions/features by maintaining more information.
4. Model Development: Implementation of Classification alogrithms for the new dataset (afterDimensinality Reduction) Like Loigtic Regression, Decision Tree and Random
5. Model Evaluation:Evaluate how the model has been performed both before (PCA Applied) and after using PCA with some of the performance matrixes like accuracy, precision, recall, F1-score etc.

The results demonstrate that Principal Component Analysis good enhanced the performance of the classification models by effectively mitigating overfitting and improving generalisation. This study emphasises the significance of dimensionality reduction techniques in machine learning and their influence on model performance.